Consider a policymaker who wants to decide which intervention to perform in order to change a currently undesirable situation. The policymaker has at her disposal a team of experts, each with their own understanding of the causal dependencies between different factors contributing to the outcome. The policymaker has varying degrees of confidence in the experts' opinions. She wants to combine their opinions in order to decide on the most effective intervention. We formally define the notion of an effective intervention, and then consider how experts' causal judgments can be combined in order to determine the most effective intervention. We define a notion of two causal models being \emph{compatible}, and show how compatible causal models can be merged. We then use it as the basis for combining experts' causal judgments. We also provide a definition of decomposition for causal models to cater for cases when models are incompatible. We illustrate our approach on a number of real-life examples.

中文翻译：

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结合专家的因果判断

考虑一个政策制定者，他希望决定采取哪种干预措施来改变当前不受欢迎的情况。决策者拥有一个专家团队，每个专家都对导致结果的不同因素之间的因果关系有自己的理解。决策者对专家的意见有不同程度的信心。她想结合他们的意见来决定最有效的干预措施。我们正式定义了有效干预的概念，然后考虑如何结合专家的因果判断以确定最有效的干预。我们定义了两个因果模型是 \emph{compatible} 的概念，并展示了如何合并兼容的因果模型。然后我们将其作为结合专家因果判断的依据。我们还提供了因果模型分解的定义，以适应模型不兼容的情况。我们用许多现实生活中的例子来说明我们的方法。